System for wireless push and pull based services

ABSTRACT

The present invention relates to a method and system for providing Web content from pull and push based services running on Web content providers to mobile users. A proxy gateway connects the mobile users to the Web content providers. A prefetching module is used at the proxy gateway to optimize performance of the pull services by reducing average access latency. The average access latency can be reduced by using at least three factors: one related to the frequency of access to the pull content; second, the update cycle of the pull content determined by the Web content providers; and third, the response delay for fetching pull content from the content provider to the proxy gateway. Pull content, such as documents, having the greatest average access latency are sorted and a predetermined number of the documents are prefetched into the cache. Push services are optimized by iteratively estimating a state of each of the mobile users to determine relevant push content to be forward to the mobile user.

CROSS REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. patent application Ser. No.09/591,746, filed Jun. 12, 2000, now U.S. Pat. No. 7,058,691 theentirety of which is hereby incorporated by reference into thisapplication.

BACKGROUND OF THE INVENTION

1. Technical Field

The invention relates to a proxy gateway for providing improved push andpull based services from a content provider on the Internet to a mobileuser on a wireless network.

2. Description of Related Art

The Internet is a global network formed by the cooperativeinterconnection of computing networks. The Worldwide Web (WWW or Web) isa collection of files or “Web pages” of text, graphics and other mediawhich are connected by hyperlinks to other Web pages which physicallyreside on the Internet. In a transaction on the WWW, a Web clienttypically requests information from a Web server. The requestedinformation is transmitted from the Web server to the Web client overthe Internet. Dramatically increasing expansion of Internet servicesusing the WWW has led to increased Web traffic.

A conventional technique to reduce Web traffic and speed up Web accessis to store copies of documents in a cache. U.S. Pat. No. 5,873,100describes an Internet browser which includes an embedded cache for usercontrolled document retention. The cache stores a plurality ofdocuments. At least one of the documents stored in the cache isdesignated as a keep document. If the storage limit of the cache isexceeded, the cache deletes the oldest document not designated as a keepdocument.

Web servers and Web client use hypertext transfer protocol (HTTP)/1.1which includes cache control features. See R. Fielding et al.,“Hypertext Transport Protocol HTTP/1.1” Network Working Group RFC, May1996, URL: ftp://ftp.isi.edu/in-notes/rfc2068.txt. The original Webserver assigns expiration times to responses generated for Web clientrequests. An expiration judgment is performed in the cache when a cachedentry is requested by a client. If the cached entry has not expired, thecache sends the entry to the client; otherwise, it sends a conditionalrequest to the Web server. A validation check is performed at the Webserver to validate if the cached entry is still useable. If the cachedentry is useable, the Web server sends a validator to the Web client;otherwise, it sends an updated response.

In certain systems, there exists very little local memory. In thesesystems, caching and prefetching is preferably performed at a proxyserver intermediate between the Web server and the Web client.Prefetching is a technique in which additional items are fetched when arequest is made for a particular item. U.S. Pat. No. 5,925,100 describesa system having a dumb client environment in which a smart serverdetermines when to send a prefetched object to the user. The prefetchedobjects are determined based on an object based prefetch primitivepresent in the client's executing application.

Other conventional prefetch schemes are based on predicting at a giventime the likelihood that a given document will be accessed in the nearfuture. Prefetch schemes have been described in which a predictionmodule computes the access probability that a file will be requested inthe near future. Each file whose access probability exceeds a server'sprefetch threshold is prefetched. See Z. Jiang and L. Kleinrock, “AnAdaptive Network Prefetch Scheme,” IEEE J. on Selec. Areas in Common.,vol. 16, no. 3, April 1998, pp. 358-368, and Z. Jiang and L. Kleinrock,“Web Prefetching in a Client Environment,” IEEE Personal Communications,vol. 5, no. 5, October 1998, pp. 25-34.

In addition, prefetch schemes have been described which are based onpopularity based prefetching. See E. P. Markatos, “Main Memory Cachingof Web Documents,” Computer Networks and ISDN Systems, vol. 28, issues7-11, pp. 893-906, 1996. Main menu caching of frequently requesteddocuments is performed on the Web server. Similarly, U.S. Pat. No.5,991,306 describes a pull based intelligent caching system fordelivering data over the Internet. Content of frequently requesteddocuments is downloaded from the content provider and cached at a localservice provider. The content is cached prior to a peak time whensubscribers are likely to request the content. A pattern recognizerdetects behavior patterns based on subscriber requests to determinewhich content the subscribers are most likely to request and when. Whencontent is finally requested, the data is streamed continuously forrendering at the subscriber computer.

Another prefetching scheme type is based on interactive prefetching inwhich prefetching is determined by the interaction between the clientand the server. For example, an interactive prefetching scheme has beenproposed in which the system gathers references by passing hypertextmarkup language (HTML) in the referenced page and collecting referencedpages with each request. See K. Chinen and S. Yamaguchi, “An InteractivePrefetching Proxy Server for Improvement of WWW Latency,” INET'97, KualaLumpur, Malaysia, 1997.

Wireless systems and mobile users are typically limited to smallbandwidth and small memory. A wireless application protocol (WAP) hasbeen developed to promote industry-wide specifications for technologyuseful in developing applications and services, such as Internetservices, that operate over wireless communication networks, asdescribed in Wireless Architecture Protocol Specification, WirelessApplication Protocol Forum, Ltd., Version 30, April 1998 and WAP PushArchitectural Overview, Version 08, November 1999. WAP framework definespull technology as a transaction initiated by the client to pullinformation from a server. For example, the World Wide Web is an exampleof pull technology in which a user enters a universal resource locator(URL) which is sent to the server and the server sends a Web page to theuser. WAP framework defines push technology as a transmission to theclient without previous action by the client. Accordingly, the serverpushes information to the client without an explicit request from theclient. It is desirable to provide a system for wireless push and pullbased Internet services which expeditiously allows users to gain accessto desired Web information.

SUMMARY OF THE INVENTION

The present invention relates to a method and system for providing Webcontent from pull and push based services running on Web contentproviders to mobile users. A proxy gateway connects the mobile users tothe Web content providers. A prefetching module is used at the proxygateway to optimize performance of the pull services by reducing averageaccess latency. The prefetch module prioritizes pull content to bestored in a cache at the proxy gateway. The average access latency canbe reduced by using at least one factor related to the frequency ofaccess to the pull content, the update cycle of the pull contentdetermined by the Web content providers and the response delay forfetching pull content from the content provider to the proxy gateway.Pull content, such as documents, having the greatest average accesslatency are sorted and a predetermined number of the documents areprefetched into the cache. Push services are optimized by iterativelyestimating a state of each of the mobile users to determine relevantpush content to be forward to the mobile user. The estimate of the stateof each mobile user can be determined from tracking information of themobile user and geo-location measurement and behavior observation data.

The invention will be more fully described by reference to the followingdrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of a system for providing push and pullbased services to mobile users.

FIG. 2 is a flow diagram of an implementation of a prefetch module usedin a proxy gateway of the system.

FIG. 3 is an illustration of a caching model.

FIG. 4 is a schematic diagram for providing push services of the system.

FIG. 5 is a flow diagram of an implementation of the mobile stateprediction.

DETAILED DESCRIPTION

Reference will now be made in greater detail to a preferred embodimentof the invention, an example of which is illustrated in the accompanyingdrawings. Wherever possible, the same reference numerals will be usedthroughout the drawings and the description to refer to the same or likeparts.

FIG. 1 illustrates a schematic diagram of a system for providing pushand pull based services to mobile users 10. Mobile users 12 a-12 n areconnected by mobile network 11 to proxy gateway 13. For example, mobilenetwork 11 is a wireless network. Web server 14 is connected by network15 to proxy gateway 13. For example, network 15 can be a wired network,such as the Internet.

Each of mobile users 12 a-12 n can interact with pull service 16 andpush service 20 running on content provider 14, such as a Web server. Asan example of pull service 16, mobile user 12 a generates a pull request17 which is transmitted over network 11 to gateway 13. Pull request 17is transmitted via proxy gateway 13 to pull service 16. Mobile user 12 areceives pull response 18 generated by pull service 16 at contentprovider 14 via proxy gateway 13. Pull response 18 includes pull content19 stored or generated by content provider 14. As an example of pushservice 20, push message 21 is sent by push service 20 to mobile user 12b. Push message 21 includes push content 22 stored or generated atcontent provider 14. Push message 21 is sent by content provider 14 toproxy gateway 13. Push message 21 is forwarded by proxy gateway 13 tomobile user 12 b.

Pull service 16 and push service 20 interact with cache 23, as describedbelow. Pull response 18 generated by content provider 14 can beprefetched and cached in cache 23. Thereafter, when pull request 17identifies a pull response 18 stored in cache 23, pull response 18stored in cache 23 can be forwarded to mobile user 12 a, or any othermobile user 12 b-12 n, without contacting content provider 14. Prefetchmodule 24 reduces access latency of pull service 16 using documentaccess log data 25 to determine prefetch control information 26 forprefetching a predetermined number of pull responses 18 into cache 23.Unprefetched pull response 18 is cached by using a conventional cachingmechanism, such as HTTP/1.1 caching. Mobile state estimation pedicationmodule 27 uses mobile tracking data 28 and geo-location measurement andbehavior observation data 29 to determine push control information 30 afor controlling caching of push messages 21 in cache 23 and fordetermining timing for sending relevant push content to the mobile user30 b depending on the state of mobile user 12 b. It will be appreciatedthat multiple pull services 16 and push services 20 can be provided atcontent provider 14 or a plurality of content providers 14 and accessedby multiple mobile users 12 a-12 n. Pull content 19 and push content 22can comprise, for example, documents including Hypertext Markup Language(HTML) files, Java files, and embedded data including images, videofiles and audio files.

FIG. 2 illustrates a flow diagram for performing prefetch module 24.Prefetch module 24 optimizes performance of pull service 16 by reducingaverage access latency using at least one factor related to thefrequency of access to pull content 19, the update cycle of pull content19, and the response delay for fetching pull content 19 from Web server14 to cache 23. Access latency is defined as the time between when pullrequest 17 is transmitted from a mobile user 12 a-12 n to the time whenpull response 18 is received at a mobile user 12 a-12 n. Web server 14generates content header 31 which is forwarded as a header of pullresponse 18. For example, content header 31 can include fields asdefined in HTTP/1.1 such as: Expires time, relating to the expirationtime of pull content 19; Last-Modified time, relating to the time pullcontent 19 was last modified at Web server 14; and Content-Length,relating to the size of pull content 19. Proxy gateway 13 receivescontent header 31 and stores fields of content header 31 in documentaccess log data 25. Proxy gateway 13 also receives pull request 17 andstores fields related to pull request 17. Fields related to cache 23 arealso stored in document access log data 25. For example, fields storedin document access log data 25 can include fields such as: request_time,related to the local time when proxy gateway 13 made pull request 17 tocontent provider 14; and response_time, related to the local time whencache 23 received pull response 18 from content provider 14.

In block 32, variables related to pull service 16 are extracted fromdocument access log data 25. For example, the extraction block 35 cancalculate the parameters associated with potential candidates that maybe included into the list of the prefetched documents. The document thathas only one access record (i.e., no revisit) in the log or has not morethan one update cycle during the statistic period is not treated as apotential candidate. Let R_(n) be the average rate of access to pullcontent 19. Pull content 19 is referred to as document n. R_(n) can bedetermined at proxy gateway 13 based on log data of cache 23 and contentheader 31 of pull responses 18 from content providers 14 and stored indocument access log data 25. Let s_(n) be the size of document n. Forexample, s_(n) can be determined from the Content-Length field forwardedby content provider 14 and stored in document access log data 25.

Let ΔT_(n) be the average response delay imposed by network 15 which canbe measured by the time from when pull request 17 from a mobile user 12a-12 n is reformatted and forwarded by proxy gateway 13 to contentprovider 14 to when pull content 19 is fetched from or validated bycontent provider 14 to cache 23, i.e. which can be measured by thedifference between the logged request_time and response_time. T_(s,n) isthe average time delay imposed by network 11 and network 15, which isthe time from when, pull request 17 is generated from mobile user 12 awhere pull request 17 is reformatted and is forwarded by proxy gateway13 to content provider 14 and pull content 19 is fetched from orvalidated by content provider 14 as pull response 18 to proxy gateway 13to when response 18 is received by mobile user 12 a. T_(c,n) is theaverage time delay imposed by network 11, which is the time between thegeneration of pull request 17 from mobile user 12 a and the reception ofpull response 18 by mobile user 12 a when a valid copy of pull content19, document n, is found in cache 23, including transmission time ofpull response 18, roundtrip time from cache 23 to mobile user 12 a andcache 23 processing time. Approximately, ΔT_(n)=T_(s,n)−T_(c,n). Letμ_(n) be the update cycle of pull content 19, document n, which is theaverage length of time between two successive expiration times or twosuccessive modifications of pull content 19, document n.

The caching model of HTTP/1.1 is illustrated in FIG. 3 in which t_(k)represents the Last_Modified time and “°” represents the Expires time ofpull content 19, document n generated by Web server 14. Cache 23 isaccessed by a group of mobile users 12 a-12 n and N is the total numberof documents n residing in various Web servers 14 with n=1, . . . N.Pull request 17 a generated by one of mobile users 12 a-12 n andforwarded to cache 23 in cycle, μ_(n), cannot be satisfied by cache 23which is denoted by missing 1. Cache 23 fetches a copy of pull content19 for pull request 17 a from Web server 14. Consequent pull requests 17b and 17 c generated by one of mobile users 12 a-12 n in cycle, μ_(n),are satisfied by cache 23 which is denoted by hit 1 and hit 2.Thereafter, in a second cycle, μ_(n), pull request 17 d generated by oneof mobile users 12 a-12 n and forwarded to cache 23 in cycle, μ_(n),cannot be satisfied by cache 23 which is denoted by missing 2. Cache 23fetches a fresh copy of pull content 19 for pull request 17 d from Webserver 14. Consequent pull requests 17 e-g generated by one of mobileusers 12 a-12 n in cycle, μ_(n), are satisfied by cache 23 which aredenoted respectively by hits 3-5. In a third cycle, μ_(n), pull request17 h generated by one of mobile users 12 a-12 n and forwarded to cache23 in cycle, μ_(n), cannot be satisfied by cache 23 which is denoted bymissing 3. Thereafter, pull request 17 i generated by one of mobileusers 12 a-12 n arrives at cache 23 between the expiration time and endof cycle, μ_(n). In this case, cache 23 validates pull content 19 at Webserver 14, represented by validate 1, before using pull content 19stored in cache 23. The distribution of interarrival time of pullrequests 17 a-17 i to pull content 19 represented by document n isrepresented by f_(n)(t) which can be an exponential distribution. SinceWeb server 14 typically specifies the expires time based on its scheduleto the end of cycle, μ_(n), the interval between the Expires time andthe end of the cycle can have a stochastic or deterministicdistribution.

In block 34 of FIG. 2, the access probability of access to document n,represented by γ_(n) is determined by:γ_(n) =R _(n) /R  (1)

wherein R is the total rate of access traffic on network 15 from gateway13, which is the sum of R_(n) for n=1, 2, . . . , N.

In block 36, the average hit rate for document n, represented by h_(n),is determined by:

$\begin{matrix}{{h_{n} = {1 - \frac{g_{n}}{R_{n}\mu_{n}}}},{n = 1},2,\ldots\mspace{11mu},N,} & (2)\end{matrix}$

in which:

-   -   g_(n) is the probability that there is at least one request to        document n during a given update cycle, μ_(n), given by:        g _(n)=1−e ^(−Rnμn) , n=1, 2, . . . , N,  (3)    -    and

R_(n)μ_(n) is the expected number of accesses to document n in an updatecycle of document n.

In block 38, the wired network access latency, represented by η_(n),imposed by network 15 when the request is the first one for document nin the update cycle μ_(n) or Expires time for document n has beenexceeded, as shown in FIG. 3, is computed from:η_(n)=γ_(n)(1−h _(n))ΔT _(n) , n=1, . . . , N,  (4)

The values at η_(n), n=1, . . . , N are sorted in descending order withdocument 1 having the greatest average latency imposed by network 15labeled as η₁, and document N having the least average latency labeledas η_(N) and relabeled as: η₁≧η₂≧η₃≧ . . . ≧η_(N).

In block 40, the total number of documents n to be prefetched to cache23, represented by r, is determined by considering at least one factor.Examples of factors include: spare capacity of cache 23 that can beutilized by the prefetching after providing sufficient capacity forconventional caching, ΔC; spare transmission bandwidth, ΔB, of network15; and desired improvement of hit probability, ΔH. The total number ofdocuments to be prefetched represented by r satisfies all of thefollowing constraints:

The constraint of spare cache capacity, ΔC, is given by

$\begin{matrix}{{{\sum\limits_{n = 1}^{r}{s_{n}\left( {1 - h_{n}} \right)}} \leq {\Delta\; C}};} & (5)\end{matrix}$where

${{\Delta\; C} \approx {C - {\sum\limits_{n = 1}^{N}{s_{n}h_{n}}}}},$C is given capacity of cache 23 and

$\sum\limits_{n = 1}^{N}{s_{n}h_{n}}$is the capacity required for conventional caching such as described inthe caching model of HTTP/1.1.

The constraint of spare transmission bandwidth, ΔB, on network 15 isgiven by:

$\begin{matrix}{{{\sum\limits_{n = 1}^{r}{\left( {1 - g_{n}} \right)\frac{s_{n}}{\mu_{n}}}} \leq {\Delta\; B}};} & (6)\end{matrix}$

${{{where}\mspace{14mu}\Delta\; B} \approx {B - {\sum\limits_{n - 1}^{N}{g_{n}\frac{s_{n}}{\mu_{n}}}}}},$B is given bandwidth and

$\sum\limits_{n - 1}^{N}{g_{n}\frac{s_{n}}{\mu_{n}}}$is the bandwidth required for conventional caching.

The constraint of desired minimum improvement of hit probability, ΔH, isgiven by:

$\begin{matrix}{{\sum\limits_{n = 1}^{r}{\gamma_{n}\left( {1 - h_{n}} \right)}} \geq {\Delta\; H}} & (7)\end{matrix}$where

${{\Delta\; H} \approx {H - {\sum\limits_{n = 1}^{N}{\gamma_{n}h_{n}}}}},$H is given hit probability, and

$\sum\limits_{n = 1}^{N}{\gamma_{n}h_{n}}$is the total average hit probability for conventional caching.

In block 41, the total number of documents to be prefetched to cache 23,r, that correspond to the r largest are selected and relabeled as η₁, .. . , η_(r). In block 42, the documents determined in block 41 areprefetched into cache 23 at proxy gateway 13 as soon as the documentsexpire at cache 23, which means the prefetched document n in cache 23 isupdated with average cycle, μ_(n), as shown in block 43. Thereafter,blocks 32-43 are repeated with a given period, such as several hours,several days, or several times the expected update cycle, μ, isrepresented by:

$\begin{matrix}{\overset{\_}{\mu} = {\sum\limits_{h = 1}^{N}{\gamma_{n}\;\mu_{n}}}} & \left( 8^{\prime} \right)\end{matrix}$

Accordingly, average latency for pull service 16 is derived as:

$\begin{matrix}{L = {{\sum\limits_{n - 1}^{N}{\gamma_{n}\left\lbrack {{h_{n}T_{c,n}} + {\left( {1 - h_{n}} \right)T_{s,n}}} \right\rbrack}} - {\sum\limits_{n - 1}^{r}\eta_{n}}}} & (8)\end{matrix}$

In Eq. (8), the first term is the latency of a conventional cache schemeand is independent of the prefetch scheme used and the second term

$\sum\limits_{n - 1}^{r}\eta_{n}$is the latency reduction as a result of prefetch scheme of the presentinvention. The latency reduction is determined by the number r and theselection of r prefetched documents. Thus, performance of blocks 32-43minimizes the average latency L.

FIG. 4 illustrates a schematic diagram for providing push services ofthe system. Movement and behavior of the mobile user 50 is measured bygeo-location measurement and behavior observation block 29. An exampleof movement and behavior of the mobile user data 50 is represented inTable 1 illustrating examples of different states a mobile user 12 a-12n can occupy.

TABLE 1 Mobile States Position Mean dwell and time in State DescriptionBehavior Time Speed Direction the state State 0 Inactive (power off — —— — d₀ or out off location- dependent services). State 1 Walking on astreet. X₁ t₁ ≈1 m/s along one d₁ direction State 2 In a shopping mall.X₂ open ≈0 d₂ hours State 3 Drive on a highway. X₃ t₃ ≈30 m/s along oned₃ direction | | | State M-1 d_(MH)

State 0 is an inactive state in which mobile user 12 a-12 n can occupywhen powering off its mobile terminal or is out of location-dependentservices of wireless network 11. State 1 to State M−1 are active stateswhich mobile user 12 a-12 n can occupy while being actively involvedwith network 11 and interacting with pull service 16 and push service20. Each state is determined by several parameters such as: position andbehavior of mobile user 12 a-12 n, at time t represented by X_(t), timeof determining state, represented by t; speed of mobile user 12 a-12 n;direction of movement of mobile user 12 a-12 n; and mean dwell time instate m, represented by d_(m) wherein M is the total number of distinctstates defined for mobile user 12 a-12 n, and m=0, 1, . . . , M−1. Y_(t)represents the results of geo-location position measurement and behaviorobservations of mobile user 12 a-12 n at time t. It is observed thatY_(t) is the observed or measured value of the position and behavior ofmobile user 12 a-12 n and is in general different from X_(t) which isthe true, but unknown, position and behavior because of geo-location andestimation error. Geo-location position and behavior data 29 can beestimated with conventional methods as described in J. H. Reed, K. J.Krizman, B. D. Woerner and T. S. Rappaport, “An Overview of theChallenges and Progress in Meeting the E-911 Requirement for LocationService”, IEEE Communications Magazine, Vol. 36, No. 4, April 1998, pp.30-37, hereby incorporated by reference into this application.

S_(t) is the state of mobile user 12 a-12 n at time t wherein S_(t)ε{0,1, . . . , M−1}.

Mobile user 12 a-12 n can transit from one state to another. The transitmobility of one of mobile users 12 a-12 n, mobile user 12, can berepresented by an M-state Markov chain with transition probabilitymatrix (TPM):P=[P _(mn)]_(MXM)  (9)

where the P_(mn) is the probability of transition from state m to staten, and m, n=0, 1, . . . , M−1.

Tracking data 28 represents sequence data of mobile user 12 over time.Tracking data 28 includes Y₁ ^(t) and {α_(τ)(m), τ=1, . . . , t, m=1, .. . , m−1}

S₁ ^(t) represents the state sequence of mobile user 12 from time 1 tot. X₁ ^(t) represents the corresponding position and behavior sequenceof mobile user 12 from time 1 to t.

Y₁ ^(t) represents the corresponding geo-location and observationsequence of mobile user 12 from time 1 to t. Mobile state predictionmodule 27 can generate push control in information 30 for controllingcaching of push content 22 and for determining timing for sending pushcontent 30 b in cache 23 to mobile user 12 based on determined states.

FIG. 5 is a flow diagram of an implementation of mobile state predictionmodule 27. Mobile state prediction module 27 estimates the posterioriprobability of the states of mobile user 12 a-12 n for a givengeo-location and observation sequence, Y₁ ^(t).

In block 60, the initial state of mobile user 12 a-12 n is defined uponregistration of mobile user 12 a-12 n in system 10. Let α_(t)(m)represent a forward variable for state sequence estimation which is aprobability that the State at time t is m and the correspondinggeo-location and observation sequence is Y₁ ^(t).

The forward variables for state sequence estimation for mobile user 12in the initial time are:α₀(0)=1, α₀(m)=0, for m=1, 2, . . . , M−1.

In block 62, a measured or observed value of a current geo-locationposition and behavior of mobile user 12, Y_(t), is determined fordetermining geo-location measurement and behavior observation data 29.

In block 63, the probability Pr{Y_(t)|X} that the geo-location measuredresult is Y_(t) when mobile user 12 position and behavior is X ispredetermined by the geolocation and observation error distribution.

In blocks 65 the values required for iteration are stored in a databasewhich values can be mobile tracking data 28. In block 64, state sequenceestimation variable α_(t)(m) for all m=1, 2 . . . M−1 is determined,which is stored into the database for the next recursive computation.

The following analysis can be used for performing block 64. At time t,tracking data is represented by: Y₁ ^(t)=(Y₁, Y₂, . . . , Y_(t-1),Y_(t)) where Y_(t) is the current measured data of the position and thebehavior of mobile user 12 from the geo-location measurement and thecollected information of proxy gateway 13. α_(t)(m) is computed for allm=1, 2, . . . , M−1, by iterations from 1 through t from the following:

$\begin{matrix}{{{\alpha_{t}(m)} = {\sum\limits_{m^{\prime} = 0}^{M - 1}{\Pr\left\{ {{S_{t - 1} = m^{\prime}};{S_{t} = m};Y_{1}^{t}} \right\}}}},} & (10) \\{\mspace{59mu}{{= {\sum\limits_{m^{\prime} = 0}^{M - 1}{\Pr\left\{ {{S_{t - 1} = m^{\prime}};Y_{1}^{t - 1}} \right\}\mspace{11mu}\Pr\mspace{11mu}\left\{ {{S_{t} = m};{\left. Y_{t} \middle| S_{t - 1} \right. = m^{\prime}}} \right\}}}},}} & (11) \\{\mspace{59mu}{= {\sum\limits_{m^{\prime} = 0}^{M - 1}{{\alpha_{t - 1}\left( m^{\prime} \right)}\mspace{11mu} p_{m^{\prime}m}\mspace{11mu}{\sum\limits_{x}{\Pr\left\{ x \middle| m \right\}\;\Pr\left\{ Y_{t} \middle| x \right\}}}}}}} & (12)\end{matrix}$

wherein p_(m′m) is the state transition probability of mobile user 12,Pr{x|m} is the probability that the mobile user locates at position andbehavior as x when it is in state m at time t, for example the outputprobability of the Markov source, and Pr{Y_(t)|x} is the probabilitythat the geo-location measured result is Y_(t) when the mobile'sposition and behavior is x at time t.

In block 66, state z is determined by:

$\begin{matrix}{{z = {\arg\mspace{11mu}{\max\limits_{m}\;\left\{ {{\left. {\Pr\left\{ {S_{t} = \left. m \middle| Y_{1}^{t} \right.} \right\}} \middle| m \right. = 1},2,\ldots\mspace{11mu},{M - 1}} \right\}}}},} & (13) \\{\mspace{11mu}{{= {\arg\mspace{11mu}{\max\limits_{m}\;\left\{ {{\left. \frac{\Pr\left\{ {{S_{t} = m};Y_{1}^{t}} \right\}}{\Pr\left\{ Y_{1}^{t} \right\}} \middle| m \right. = 1},2,\ldots\mspace{11mu},{M - 1}} \right\}}}},}} & (14) \\{\mspace{11mu}{= {\arg\mspace{11mu}{\max\limits_{m}\;{\left\{ {{\left. {\alpha_{t}(m)} \middle| m \right. = 1},2,\ldots\mspace{11mu},{M - 1}} \right\}.}}}}} & (15)\end{matrix}$

Accordingly, blocks 60, 63, 64 and 66 determine mobile state predictionmodule 27.

In block 68, state z related push content 22 is pushed to mobile user 12a-12 n as relevant push content to the mobile user 30 b. In block 69,mobile user 12 may accept or reject push content 22 and this behavior isobserved by block 62. The mobile user's current behavior and position isforwarded to block 62 and block 62-69 can be repeated.

It is to be understood that the above-described embodiments areillustrative of only a few of the many possible specific embodimentswhich can represent applications of the principles of the invention.Numerous and varied other arrangements can be readily devised inaccordance with these principles by those skilled in the art withoutdeparting from the spirit and scope of the invention.

1. A method for optimizing performance of at least one pull service andat least one push service to a plurality of mobile users comprising thesteps of: reducing access latency for said at least one pull servicerunning on at least one Web server to optimize said at least one pullservice by prefetching documents into a cache of at least one proxygateway by using factors of a frequency of access of said plurality ofmobile users to said pull content of said pull service, an update cycleof said pull content, said update cycle of pull content is the averagelength of time between two successive expiration times or two successivemodifications of said pull content and response delay for fetching saidpull content from said at least one Web server to said at least oneproxy gateway, said at least one proxy gateway connected between saidmobile user and said Web server; and iteratively estimating a state ofeach of said plurality of mobile users for determining push content tobe forwarded to said mobile user by said at least one push servicerunning on said at least one Web server to optimize said at least onepush service; wherein said step of reducing access latency comprises thestep of selecting a predetermined number of documents to be prefetchedinto cache of a proxy gateway, and said step of selecting apredetermined number of documents uses said factors of: said frequencyof access, said update cycle and said response delay, wherein saidfrequently accessed pull documents having a shorter update cycle and alonger response delay are prioritized for being prefetched in said cacheof said proxy gateway; wherein said step of iteratively estimating astate of each of said plurality of mobile users is determined fromtracking data of said plurality of mobile users and geo-locationmeasurement and behavior observation data.
 2. The method of claim 1wherein said pull content is plurality of documents and said step ofreducing access latency further comprises the step of selecting apredetermined number of documents to be prefetched into said cache ofsaid proxy gateway, wherein said predetermined number of documents havethe greatest reduction in said access latency.
 3. The method of claim 1wherein said step of reducing access latency uses said factor of saidfrequency of access wherein frequently accessed documents areprioritized for being stored in a-said cache of a-said at least oneproxy gateway.
 4. The method of claim 1 wherein said step of reducingaccess latency uses said factor of said update cycle wherein said pulldocuments having a shorter update cycle are prioritized for being storedinto a-said cache of said at least one proxy gateway.
 5. The method ofclaim 1 wherein said access latency uses said factor of said responsedelay wherein said pull documents having a longer response delay areprioritized for being stored in said cache of said at least one proxygateway.
 6. The method of claim 1 further comprising the step of:caching mobility and behavior-related push content into said cache ofsaid at least one proxy gateway.
 7. The method of claim 6 wherein saidstate of each of said plurality of mobile users is determined from atleast one of the following factors: location of said one of saidplurality of mobile users, direction of said one of said plurality ofmobile users, speed of said one of said plurality of mobile users, andbehavior of said one of plurality of mobile users.
 8. A system forproviding optimization of performance at least one pull service and atleast one push service to a plurality of mobile users comprising: meansfor reducing access latency for said at least one pull service runningon at least one Web server by prefetching documents into a cache of aproxy gateway, said proxy gateway being connected between said mobileuser and said pull service and push service by using factors of afrequency of access of said plurality of mobile users to said pullcontent of said pull service, an update cycle of said pull content, saidupdate cycle of pull content is the average length of time between twosuccessive expiration times or two successive modifications of said pullcontent and response delay for fetching said pull content from said atleast one Web server to said at least one proxy gateway said at leastone proxy gateway connected between said mobile user and said Webserver; and means for iteratively estimating a state of each of saidplurality of mobile users for determining push content to be forwardedto said mobile user by said at least one push service running on said atleast one Web server to optimize said at least one push service; whereinsaid pull content is a plurality of documents and said means forreducing access latency comprises the step of: selecting a predeterminednumber of documents to be prefetched into cache of a proxy gateway, andselecting a predetermined number of documents uses said factors of: saidfrequency of access, said update cycle and said response delay, whereinsaid frequently accessed pull documents having a shorter update cycleand a longer response delay are prioritized for being prefetched in saidcache of said proxy gateway; wherein said means for iterativelyestimating a state of each of said plurality of mobile users isdetermined from tracking data of said plurality of mobile users andgeo-location measurement and behavior observation data.
 9. The system ofclaim 8 further comprising: means for controlling of caching mobilityand behavior-related push content into said cache of said at least oneproxy gateway.
 10. The system of claim 9 wherein each-said state ofone-each of said mobile users is determined from at least one of thefollowing factors: location of said one of said plurality of mobileusers, direction of said one of said plurality of mobile users, speed ofsaid one of said plurality of mobile users, and behavior of said one ofplurality of mobile users.